Senior Forward Deployed Engineer, Open Models

Responsibilities

• This role drives AI solutions from discovery and scoping through post-training, harness optimization, and deployment, working alongside partners and customers. Each use case becomes a post-training and harness strategy with the right methods, data, evaluation, and success criteria. Scalable architectures and deployment…
• The work is hands-on, as we build together with the ecosystem. Custom AI takes skill across the full stack, and we work across all of it: designing evaluations, building synthetic data pipelines, and post-training with LoRA and other parameter-efficient methods, supervised fine-tuning, and reinforcement learning…
• You will lead technical engagements by scoping the work, shaping statements of work, and driving delivery with partner engineering teams and joint customers. We scale our impact by building reusable recipes, reference pipelines, and playbooks that let partners succeed independently and help the ecosystem mature in…
• We connect NVIDIA's model research to partner and customer use cases, unlocking the post-trainability and inference cost-effectiveness of our models, and work with our product, research, and engineering teams to bring field insights back into Nemotron, Cosmos, and our software stack.
• Above all, we're excited to help organizations worldwide build, own, and run their own AI on open source models!
• What we need to see
• Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent experience.
• 8+ years in ML or software engineering, applied AI, FDE, or solutions architecture, including 3–5+ years of hands-on LLM post-training and taking models to production.
• Solid fundamentals in deep learning and machine learning. Deep expertise in post-training methods, including LoRA/PEFT, SFT, and reinforcement learning, with an understanding of the trade-offs between RL synchronous and asynchronous approaches. Strong grasp of model architectures and hyperparameters and how they shape…
• Good understanding of large-scale distributed training and inference performance optimization. Ability to configure and tune workloads for memory efficiency, throughput, latency, and scaling across multi-GPU and multi-node systems.

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